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Worldwide Parcel Tracking System (Web & Mobile Platform)
1. Introduction
With the rapid growth of eCommerce, logistics, food delivery, and cross-border shipments, customers and businesses demand real-time, reliable parcel tracking. Currently, tracking information is fragmented across courier websites, difficult to understand, and not always mobile-friendly.
This project proposes the development of a Worldwide Parcel Tracking System that allows users to track local and international parcels using a single platform. The system will support tracking from major global marketplaces and courier services such as Amazon, AliExpress, eBay, ASOS, Shein, as well as Sri Lanka Post and international postal networks.
2. Problem Statement
Manual checking of multiple courier websites wastes time.
Customers often do not understand shipment statuses.
Small businesses lack professional tracking tools.
International shipments have delayed or unclear updates.
No unified tracking solution for Sri Lanka–focused and global parcels.
3. Proposed Solution
The proposed system is a centralized parcel tracking platform that provides:
One-click tracking using a Tracking / Waybill / Order ID
Real-time shipment status updates
Global courier and postal integration
Mobile-friendly design with live tracking
Admin and business dashboards for shipment management
The platform will be accessible via web and mobile (Android) and designed for speed, simplicity, and convenience.
4. Objectives
Provide a single tracking interface for local and international parcels
Support global postal and courier services
Improve shipment transparency for customers
Assist small businesses and delivery services with tracking management
Offer live tracking and shipment status notifications
5. Target Users
Online shoppers (local & international)
Small businesses and shop owners
eCommerce store owners (WooCommerce, custom shops)
Food delivery services
Courier companies and logistics providers
Students (academic and final-year projects)
6. Key Features & Functionalities
6.1 Parcel Tracking
Track parcels using:
Tracking Number
Waybill Number
Order ID (12-digit reference)
Phone Number (optional)
Support for:
International parcel tracking
Sri Lanka Post tracking
eCommerce order tracking
6.2 Tracking Information Display
Shipment status (Pending, Collected, In Transit, Out for Delivery, Delivered)
Collected Date
Destination Branch
Parcel Weight Category (Kg)
Live tracking route (GPS-based – where supported)
Shipment history and events timeline
6.3 Global Courier Integration
Integration with global carriers and marketplaces:
Amazon, AliExpress, eBay, ASOS, Shein
China Post, UK Royal Mail, USPS
Sri Lanka Post
Auto-detect courier based on tracking number format
6.4 Live Tracking & Notifications
Live GPS tracking for supported shipments
Email / SMS / App notifications for status changes
Delay and exception alerts
6.5 Order Tracking Page
User-friendly tracking dialog
“Track” button for instant updates
Mobile-optimized UI
Shareable tracking link
6.6 Admin & Business Panel
Add and manage shipments manually or via Excel upload
View shipment status dashboard
Export shipment reports (Excel / CSV)
Manage routes, drivers, and delivery status
6.7 eCommerce Integration
WooCommerce plugin integration
Auto-sync order and tracking data
Tracking button on order confirmation page
Customer tracking link in email receipt
7. Mobile Application Features (Android)
Track parcels with one tap
Save favorite tracking numbers
Push notifications for shipment updates
Barcode / QR code scanning for tracking numbers
Lightweight and fast performance
8. System Architecture (High-Level)
Frontend:
Web: HTML, CSS, JavaScript (React / Vue optional)
Mobile: Android (Kotlin / Flutter)
Backend:
REST API (Node.js / PHP / Laravel)
Courier API integrations
Tracking aggregation engine
Database:
MySQL / PostgreSQL
Optional Services:
Google Maps API (Live route tracking)
SMS Gateway
Email Notification Service
9. Non-Functional Requirements
High performance and fast response
Mobile-first responsive design
Secure data handling (HTTPS, encrypted storage)
Scalable to handle thousands of tracking requests
High availability (99% uptime target)
10. Use Cases
Track my delivery online
Track international parcel from China or UK
Sri Lanka order tracking
Business shipment monitoring
Customer order tracking via receipt or email
11. Advantages of the System
Centralized tracking for all couriers
Easy-to-use interface
Saves time for customers and businesses
Improves delivery transparency
Suitable for startups, SMEs, and academic projects
12. Future Enhancements
AI-based delivery time prediction
Multi-language support
iOS mobile application
Driver mobile app
Analytics and delivery performance reports
13. Conclusion
The Worldwide Parcel Tracking System provides a modern, efficient, and scalable solution for tracking shipments locally and globally. It enhances customer experience, supports business growth, and simplifies logistics management. This project is ideal for commercial deployment as well as student final-year projects.
**Title**: **VeriGuard: A Machine
Learning–Based Fake News Detection System**
## **1. Introduction**
### **1.1 Background**
In the digital era, information spreads
at unprecedented speed through social media, messaging apps, and
online news platforms. Unfortunately, so does misinformation. Fake
news—deliberately fabricated or misleading content—can influence
public opinion, incite panic, and damage reputations. Studies show
that fake news spreads significantly faster and farther than true
stories, often due to its emotionally charged nature.
Existing fact-checking platforms (e.g.,
Snopes, FactCheck.org) rely heavily on manual verification, which is
slow and cannot scale to real-time demands. There is a critical need
for an automated, intelligent system that can analyze textual content
and flag potentially false information using machine learning (ML).
### **1.2 Problem Statement**
Fake news spreads faster than factual
reporting, especially on social media. Manual verification is
time-consuming and cannot keep pace with the volume of content
generated daily. Users lack immediate, reliable tools to assess news
credibility before sharing or acting upon it.
### **1.3 Proposed Solution**
**VeriGuard** is an AI-powered web and
mobile application that uses natural language processing (NLP) and
supervised machine learning models to detect fake news in real time.
The system analyzes article content, linguistic patterns, source
credibility, and metadata to classify news as **“Likely Real,”
“Likely Fake,”** or **“Uncertain.”** It provides users with a
credibility score, supporting evidence, and alternative verified
sources.
---
## **2. Objectives**
### **2.1 General Objective**
To design, develop, and evaluate a
scalable machine learning system that accurately detects fake news in
textual content and educates users on information credibility.
### **2.2 Specific Objectives**
1. Collect and preprocess a diverse
dataset of real and fake news articles.
2. Train and evaluate multiple ML/NLP
models (e.g., BERT, LSTM, SVM, Random Forest) for fake news
classification.
3. Develop a responsive web application
with a RESTful backend and intuitive UI.
4. Implement real-time URL/article
analysis with explainable AI (XAI) features.
5. Integrate user feedback to enable
continuous model improvement.
6. Evaluate system performance using
accuracy, precision, recall, and F1-score.
7. Ensure data privacy, low latency,
and cross-platform accessibility.
---
## **3. Scope**
### **3.1 In Scope**
- Text-based fake news detection
(English language only in v1.0).
- Web application accessible via
desktop and mobile browsers.
- URL input and direct text paste
functionality.
- Real-time classification with
confidence score (0–100%).
- Source reputation database (curated
list of trusted/untrusted domains).
- User feedback mechanism (thumbs
up/down).
- Admin dashboard for model monitoring
and dataset management.
- RESTful API for third-party
integration (e.g., browser extensions).
### **3.2 Out of Scope**
- Image, video, or audio-based
misinformation detection.
- Multilingual support (beyond
English).
- Social media account verification.
- Legal enforcement or content
takedown.
- Real-time social media feed
monitoring (e.g., Twitter/X scraping).
---
## **4. Functional Requirements**
| **ID** | **Feature** |
**Description** |
|--------|-------------|------------------|
| FR-01 | **User Registration/Login** |
Users can create accounts using email or Google SSO. |
| FR-02 | **News Submission** | Users
can submit news via URL or paste article text. |
| FR-03 | **Real-Time Analysis** |
System processes input and returns classification within 3 seconds. |
| Deployment & Documentation |
Weeks 15–16 | Live Demo, Final Report |
---
## **11. References**
1. Shu, K., et al. (2017). *Fake News
Detection on Social Media: A Data Mining Perspective*. ACM SIGKDD.
2. Wang, W. Y. (2017). *“Liar, Liar
Pants on Fire”: A New Benchmark Dataset for Fake News Detection*.
ACL.
3. TensorFlow & Hugging Face
Documentation.
4. IEEE SRS Template (IEEE Std
830-1998).
---
This document is **dissertation-ready**
and includes sufficient technical depth for implementation. You may
expand sections like **risk analysis**, **ethical considerations**,
or **detailed UML diagrams** (use case, sequence, ERD) as needed for
your final submission.
Would you like the **database schema**,
**API specification**, or **UI wireframes** next?
Order management software that helps you manage sales and purchase orders, create packages and shipments, and send delivery updates centrally. Customers can conveniently place and track their orders through our user-friendly web page, which offers real-time tracking updates, automated notifications
What is an order tracking system?
What is a delivery tracking system?
How to track a purchase order?
What is the best way to keep track of orders?
What are the 4 types of ordering systems?
What are the 7 steps of order fulfillment?
What are the 7 steps of order taking?
What are the 7 C's of logistics?
What are the steps in order tracking?
What are the 7 R's rule?
What is tracking and types?
What are 5 steps in the receiving process in order?
What is an example of tracking?
What are the 5 P's of logistics?
What is the process of tracking?
What software is best for PO tracking?
What is eCommerce order tracking?
What is the concept of tracking?
What is logistics tracking?
What is a delivery tracker?
What is a delivery management system?
What are the benefits of tracking?
How does tracking packages work?
What is an example of an order number?
Order tracking system free
Track my delivery
Citypak tracking
Parcel tracking
Prompt Express Tracking
Tracking online
Live tracking parcel
Best order tracking system
In
today’s fast-paced world, individuals often juggle multiple
responsibilities and frequently forget essential daily tasks, leading
to reduced productivity and increased stress. Traditional to-do list
apps lack intelligence—they merely store tasks without
understanding user habits, priorities, or context.
This
project proposes the development of an AI
Smart Task Manager—a
mobile and web application that uses artificial intelligence to
intelligently manage, remind, and prioritize user tasks based on
behavior patterns, time sensitivity, and contextual cues.
1.2.
Problem Statement
People
routinely forget daily tasks such as taking medication, paying bills,
attending appointments, or completing work assignments. Existing task
managers are static—they do not adapt to user behavior, miss
contextual awareness (e.g., location, time of day), and fail to
predict or suggest tasks proactively.
1.3.
Objectives
Develop
an intelligent task management system powered by AI.
Automate
task prioritization using user behavior analytics.
Provide
context-aware reminders (time, location, calendar events).
Enable
natural language input for task creation.
Reduce
cognitive load and improve task completion rates.
1.4.
Scope
The
system will:
Allow
users to add, edit, delete, and categorize tasks.
Use
AI (machine learning + NLP) to infer task urgency and deadlines.
Send
smart notifications based on user routines and external triggers
(e.g., "You’re near the pharmacy—don’t forget to pick up
your prescription").
Sync
across devices (mobile + web).
Support
voice and text input for task entry.
The
system will not:
Integrate
with third-party enterprise tools (e.g., Jira, Asana) in Phase 1.
Store
sensitive personal data beyond what’s necessary for task
management.
Replace
medical or legal scheduling systems.
1.5.
Target Users
University
students
Working
professionals
Elderly
individuals managing daily routines
Anyone
seeking an intelligent, proactive task assistant
1.6.
Technologies
Frontend:
React (Web), React Native (Mobile)
Backend:
Node.js / Django
Database:
PostgreSQL or Firebase
AI/ML:
Python (scikit-learn, spaCy, or TensorFlow Lite for on-device
inference)
NLP:
Natural Language Understanding for parsing task inputs (e.g., “Call
mom tomorrow at 5 PM” → structured task)
Cloud:
Firebase Cloud Messaging (FCM) for notifications
Deployment:
Docker, AWS/GCP
1.7.
Expected Outcomes
A
fully functional MVP with core AI-driven task management.
Improved
user task completion rate (measurable via user testing).
A
novel algorithm for dynamic task prioritization.
A
foundation for future enhancements (e.g., habit tracking, team
collaboration).
2.
Software Requirements Specification (SRS)
Based
on IEEE 830 Standard
2.1.
Introduction
2.1.1
Purpose
This
document specifies the functional and non-functional requirements for
the AI
Smart Task Manager
application, serving as a blueprint for design, development, and
testing.
2.1.2
Scope
As
outlined in the proposal, the system enables intelligent task
creation, prioritization, and reminders using AI. It supports
multi-platform access and personalization.
2.1.3
Definitions
NLP:
Natural Language Processing
ML:
Machine Learning
Task:
A unit of work with title, deadline, priority, and context
Smart
Reminder:
A context-aware notification triggered by time, location, or user
behavior
2.2.
Overall Description
2.2.1
Product Perspective
Standalone
application with cloud backend. Integrates with device calendar,
location services, and notification systems.
2.2.2
User Classes
User
Type
Description
Regular
User
Creates
and manages personal tasks
Admin
(optional)
Manages
system analytics (for research phase)
2.2.3
Operating Environment
Mobile:
Android 10+, iOS 14+
Web:
Chrome, Firefox, Safari (latest)
Internet
connectivity required for sync and AI cloud inference (optional
offline mode)
2.2.4
Assumptions & Dependencies
Users
grant location and notification permissions.
AI
model training data will be simulated or collected ethically during
testing.
Third-party
APIs: Google Maps (for geofencing), Calendar API.
Dynamically
rank tasks using ML model based on: deadline, frequency, user
history
FR5
Context-Aware
Reminders
Trigger
reminders by:<br>• Time (e.g., 9 AM)<br>•
Location (e.g., near gym)<br>• Event (e.g., after meeting
ends)
FR6
Recurring
Tasks
Support
daily/weekly/custom repeats
FR7
Task
Categories & Tags
e.g.,
Work, Health, Personal
FR8
Task
History & Analytics
Show
completion rate, missed tasks, peak productivity hours
FR9
Sync
Across Devices
Real-time
synchronization via cloud
FR10
Backup
& Export
Export
tasks as CSV or JSON
2.3.2
Non-Functional Requirements
Type
Requirement
Performance
App
loads in <2s; reminders trigger within 30s of condition
Usability
Intuitive
UI; <3 taps to add a task
Reliability
99%
uptime; local caching for offline use
Security
Data
encrypted in transit (TLS) and at rest; GDPR-compliant
Scalability
Support
10,000+ concurrent users (cloud-ready)
Maintainability
Modular
code; logging and error tracking (Sentry/LogRocket)
2.4.
AI/ML Component Specification
2.4.1
Task Prioritization Engine
Input:
Task metadata + user interaction history
Model:
Lightweight classifier (e.g., Random Forest or Logistic Regression)
Features:
Deadline
proximity
Task
category importance (user-defined)
Historical
completion rate for similar tasks
Time
of day preference
2.4.2
NLP Parser
Parses
free-text input using rule-based + ML hybrid (e.g., spaCy + custom
regex)
Extracts:
Action
verb
Object
Time
expression
Location
hint
2.4.3
Context Detection
Uses
device sensors + calendar:
Geofencing:
Trigger when user enters/leaves location
Calendar
integration:
Schedule reminders relative to events
2.5.
Comparative Analysis of Existing Systems
System
Strengths
Weaknesses
Gap
Addressed by Our System
Todoist
Clean
UI, cross-platform
No
AI; static priorities
✅ AI-driven
dynamic prioritization
Microsoft
To Do
Integrates
with Outlook
Limited
context awareness
✅
Location/time/event-based
triggers
Google
Tasks
Simple,
free
No
smart suggestions
✅ Proactive
task prediction
TickTick
Habit
tracking, Pomodoro
No
NLP for task input
✅ Natural
language task creation
Any.do
Voice
input, reminders
AI
features limited to premium
✅ Open,
intelligent core in free tier
Key
Innovation:
Our system uniquely combines
NLP task entry, behavioral learning, and multi-context reminders in a
single open architecture.
2.6.
Development Roadmap (Milestones)
Phase
Timeline
Deliverables
1.
Research & Design
Month
1
SRS,
UI mockups, architecture diagram
2.
Core Backend + Auth
Month
2
User
system, task CRUD API
3.
NLP & AI Module
Month
3
Task
parser, priority model (Python microservice)
4.
Mobile & Web Frontend
Month
4
React
Native + React apps
5.
Context Integration
Month
5
Geofencing,
calendar sync, smart reminders
6.
Testing & Evaluation
Month
6
Usability
tests, accuracy metrics, dissertation
2.7.
Evaluation Metrics
Task
Parsing Accuracy:
% of correctly structured tasks from NLP
Reminder
Relevance Score:
User rating (1–5) for smart notifications
Completion
Rate Improvement:
Before vs. after using the app (via A/B testing)
System
Latency:
Time from trigger to notification
2.8.
Risks & Mitigation
Risk
Mitigation
Overcomplicated
AI model
Start
with rule-based + simple ML; iterate
Privacy
concerns
On-device
processing where possible; clear privacy policy
Low
user engagement
Gamification
(streaks, achievements)
Platform
fragmentation
Use
cross-platform frameworks (React Native, Flutter alternative)
3.
Conclusion
The
AI
Smart Task Manager
addresses a real-world problem with an innovative, user-centric
approach. By integrating NLP, behavioral analytics, and context
awareness, it goes beyond traditional tools to deliver a truly
intelligent assistant. This SRS provides a complete foundation for
development, testing, and academic evaluation.
Next
Steps for You (Yew)
Customize
this document with your university’s formatting guidelines.
Add
diagrams:
Use Case, ERD, System Architecture (I can help generate these).
Begin
UI
wireframing
(Figma recommended).
Set
up Git
repository
with project structure.
Would
you like me to:
Generate
UML diagrams?
Provide
a sample database schema?
Write
user stories or API specs?
Help
with the dissertation structure (Chapters 1–5)?
Let
me know—I'm ready to support your BIT final project!
